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Better Software Through Measurement

Course Summary

This course will show you how to generate recommendations for your users, filter messages based on users' preferences, decide which web page performs best, keep track of timings in your application, and discover groups among items. These techniques are at

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    Course Syllabus

    ● Instrumentation: Streaming Metrics
        ◦ Measure Everything
        ◦ DEMO: Streaming Mean and Variance
        ◦ Quantiles with QDigest
        ◦ DEMO: QDigest Implementation
        ◦ Calculating Quantiles
        ◦ DEMO: Quantile Implementation
        ◦ Summary
    ● Optimizing Conversion: A/B Testing
        ◦ Introduction
        ◦ The Basic Idea
        ◦ Introducing Google Content Experiments
        ◦ Demo: Defining A Goal
        ◦ Demo: Setting up A Content Experiment
        ◦ Waiting for Results
        ◦ Demo: Estimating Sample Size
        ◦ Summary
    ● Recommendations: Item-based Recommendations
        ◦ Introduction
        ◦ Problem: Show Suggested Products
        ◦ The Fine Foods Data Set
        ◦ Recommendation Algorithm and Item Similarity
        ◦ Demo: Calculating Similarity Scores
        ◦ Demo: Top Similar Items
        ◦ Recommendations from Estimated Ratings
        ◦ Demo: Recommendations
        ◦ Further Ideas and Summary
    ● Personalized Recommendations: Naive Bayesian Classifier
        ◦ Introduction
        ◦ Problem: Personalized Links
        ◦ Demo: Displaying Links
        ◦ Naive Bayesian Classifier
        ◦ Demo: Capturing user Likes/Dislikes
        ◦ Implementing A Naive Bayesian Classifier
        ◦ Demo: Implementing The Likelihoods Function
        ◦ Demo: Sorting and Displaying Recommendations
        ◦ Summary
    ● Finding Groups: k-means Clustering
        ◦ Introduction
        ◦ Problem: Discovering Groups
        ◦ Demo: Getting A Friend List
        ◦ Demo: Sports Teams in Common
        ◦ Friend Versus Sports Team Matrix
        ◦ Demo: Creating The Friend Matrix
        ◦ Overview of k-means Clustering
        ◦ Tanimoto Distance
        ◦ Demo: Implementing The Tanimoto Distance
        ◦ Demo: Implementing k-means
        ◦ Summary


Course Fee:
USD 29

Course Type:


Course Status:



1 - 4 hours / week

This course is listed under Development & Implementations and Quality Assurance & Testing Community

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Awards & Accolades for MyTechLogy
Winner of
Top 100 Asia
Finalist at SiTF Awards 2014 under the category Best Social & Community Product
Finalist at HR Vendor of the Year 2015 Awards under the category Best Learning Management System
Finalist at HR Vendor of the Year 2015 Awards under the category Best Talent Management Software
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